Performance of aggregation pheromone system on unimodal and multimodal problems

This paper describes and analyzes the aggregation pheromone system (APS) algorithm, which extends ant colony optimization (AGO) to continuous domains. APS uses the collective behavior of individuals that communicate using aggregation of pheromones. Two variants of APS are considered: the existing generational APS and the proposed steady-state APS. Both variants of APS are tested on several common unimodal and multimodal problems and their performance on these problems is analyzed with different parameter settings. The results indicate that using a steady-state evolutionary model improves the performance of APS on both unimodal as well as multimodal problems and that the performance of APS is relatively robust with respect to its parameter settings

[1]  W. Vent,et al.  Rechenberg, Ingo, Evolutionsstrategie — Optimierung technischer Systeme nach Prinzipien der biologischen Evolution. 170 S. mit 36 Abb. Frommann‐Holzboog‐Verlag. Stuttgart 1973. Broschiert , 1975 .

[2]  Vittorio Maniezzo,et al.  Exact and Approximate Nondeterministic Tree-Search Procedures for the Quadratic Assignment Problem , 1999, INFORMS J. Comput..

[3]  Léon J. M. Rothkrantz,et al.  Ant-Based Load Balancing in Telecommunications Networks , 1996, Adapt. Behav..

[4]  Hiroshi Fukami,et al.  Aggregation arrestant pheromone of the German cockroach,Blattella germanica (L.) (Dictyoptera: Blattellidae): Isolation and structure elucidation of blattellastanoside-A and -B , 1993, Journal of Chemical Ecology.

[5]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[6]  A. Wren,et al.  An Ant System for Bus Driver Scheduling 1 , 1997 .

[7]  J. A. Lozano,et al.  Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation , 2001 .

[8]  Ingo Rechenberg,et al.  Evolutionsstrategie : Optimierung technischer Systeme nach Prinzipien der biologischen Evolution , 1973 .

[9]  David E. Goldberg,et al.  A Survey of Optimization by Building and Using Probabilistic Models , 2002, Comput. Optim. Appl..

[10]  M. Schatzman,et al.  Numerical Analysis: A Mathematical Introduction , 2002 .

[11]  Seid H. Pourtakdoust,et al.  An Extension of Ant Colony System to Continuous Optimization Problems , 2004, ANTS Workshop.

[12]  Alain Hertz,et al.  Ants can colour graphs , 1997 .

[13]  B. Bullnheimer,et al.  A NEW RANK BASED VERSION OF THE ANT SYSTEM: A COMPUTATIONAL STUDY , 1997 .

[14]  Ian C. Parmee,et al.  The Ant Colony Metaphor for Searching Continuous Design Spaces , 1995, Evolutionary Computing, AISB Workshop.

[15]  Pedro Larrañaga,et al.  Estimation of Distribution Algorithms , 2002, Genetic Algorithms and Evolutionary Computation.

[16]  Masayuki Yamamura,et al.  Theoretical Analysis of Simplex Crossover for Real-Coded Genetic Algorithms , 2000, PPSN.

[17]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[18]  Thomas Stützle,et al.  The MAX–MIN Ant System and Local Search for Combinatorial Optimization Problems: Towards Adaptive Tools for Global Optimization , 1997 .

[19]  Nicolas Monmarché,et al.  On how Pachycondyla apicalis ants suggest a new search algorithm , 2000, Future Gener. Comput. Syst..

[20]  Shigeyoshi Tsutsui,et al.  Ant Colony Optimisation for Continuous Domains with Aggregation Pheromones Metaphor , 2004 .

[21]  Isao Ono,et al.  A Real Coded Genetic Algorithm for Function Optimization Using Unimodal Normal Distributed Crossover , 1997, ICGA.

[22]  V. K. Jayaraman,et al.  Ant Colony Approach to Continuous Function Optimization , 2000 .

[23]  Johann Dréo,et al.  A New Ant Colony Algorithm Using the Heterarchical Concept Aimed at Optimization of Multiminima Continuous Functions , 2002, Ant Algorithms.

[24]  Krzysztof Socha,et al.  ACO for Continuous and Mixed-Variable Optimization , 2004, ANTS Workshop.

[25]  Richard F. Hartl,et al.  Applying the ANT System to the Vehicle Routing Problem , 1999 .

[26]  William J. Bell,et al.  Chemo-orientation in Walking Insects , 1984 .

[27]  C. Lazzari,et al.  Aggregation behaviour and interspecific responses in three species of Triatominae. , 1998, Memorias do Instituto Oswaldo Cruz.

[28]  David E. Goldberg,et al.  Real-Coded Bayesian Optimization Algorithm: Bringing the Strength of BOA into the Continuous World , 2004, GECCO.

[29]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[30]  Josef Schwarz,et al.  Estimation Distribution Algorithm for mixed continuous-discrete optimization problems , 2002 .

[31]  Luca Maria Gambardella,et al.  Ant colony system: a cooperative learning approach to the traveling salesman problem , 1997, IEEE Trans. Evol. Comput..

[32]  Jiri Ocenasek,et al.  Estimation of Distribution Algorithm for Mixed Continuous-Discrete Optimization , 2002 .

[33]  Nikolaus Hansen,et al.  Adapting arbitrary normal mutation distributions in evolution strategies: the covariance matrix adaptation , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.